The feasibility of a transportable artificial neural network (ANN)–based technique for the classification of flow regimes in three phase gas/liquid/pulp fiber systems, using pressure signals as input, was demonstrated in this study. Both supervised and unsupervised neural network models were applied for implementing regime classification. Data obtained in a vertical column (1.8m high and 5.08cm in diameter) were used, and a supervised ANN was designed and successfully tested that used some characteristics of the power density spectrum of the recorded signals of a pressure sensor as input. The developed ANN showed encouraging transportability. An ANN-based method was also developed for adjusting the processed signals of one sensor before feeding them as input to an ANN that had been trained based on data from another similar sensor. The method further improved transportability. The objectivity of the experimentally-identified flow regimes and their transition conditions was verified by the application of a Kohonen self-organizing neural network.
Artificial Neural Network-Based Flow Regime Classification Techniques for Gas-Liquid-Fiber Three-Phase Flows
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Xie, T, & Ghiaasiaan, SM. "Artificial Neural Network-Based Flow Regime Classification Techniques for Gas-Liquid-Fiber Three-Phase Flows." Proceedings of the ASME 2004 Heat Transfer/Fluids Engineering Summer Conference. Volume 3. Charlotte, North Carolina, USA. July 11–15, 2004. pp. 517-530. ASME. https://doi.org/10.1115/HT-FED2004-56227
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